Patent reference 1 discloses a coring technology that may be adopted when handling a noise signal in an image, which is normally assumed to be present in a high-frequency component. Through the coring technology, the original image is divided into a low-frequency component and a high-frequency component and high-frequency component values which, within a range of plus or minus a threshold value, are reduced to zero. Nonpatent reference 1 through 4 and patent reference 2 and 3 each disclose a technology achieved by adopting the coring method in multiple resolution wavelet transformation or Laplacian pyramid representation.
The technologies disclosed in nonpatent reference 1 through 3 are all equivalent to the so-called wavelet shrinkage technology, whereby the high pass component of a wavelet transformation coefficient is cored through threshold value processing or nonlinear threshold value processing. Patent reference 2 and 3 each disclose a coring technology achieved by executing nonlinear threshold value processing on the high pass component in Laplacian pyramid representation (i.e., the Laplacian component of the target image that has been broken down into a Gaussian component and a Laplacian component) so as to handle data assuming values close to the threshold value gently.
Nonpatent reference 4 describes nonlinear threshold value processing executed on the high pass component of a Steerable wavelet transformation coefficient that is better suited, compared to the regular orthogonal wavelet transformation, for applications in which wider directional characteristics or a wider range of rotational stability needs to be sustained. Patent reference 4 discloses a method for executing such a coring operation via an analog circuit through optimal band pass range assignments in spite of the restrictions imposed with regard to the transistor response range.
Patent reference 5 through 9 each disclose a method of removing noise contained in a high pass subband resulting from multiple resolution transformation based upon the relationship with the subband coefficient at a nearby pixel, instead of based upon the subject subband coefficient alone.
Patent reference 5 discloses a method of obtaining a noise-free image by removing noise through spatial filtering of a high-frequency subband coefficient having undergone Steerable wavelet transformation and then executing inverse transformation on the data. Patent reference 6 discloses a method whereby an order statistics filter is applied to a high-frequency subband expressed in Laplacian pyramid representation.
Patent reference 7 discloses a method of noise removal achieved by extracting a noise signal contained in a high-frequency subband coefficient in Laplacian pyramid representation based upon a local statistic value reflecting the relation with a nearby pixel and a general statistic value commonly used within the subband and then attenuating the extracted noise signal. Patent reference 8 discloses synthesis processing through which noise signals extracted from high-frequency subbands LH, HL and HH among a low-frequency subband LL and the high-frequency subbands LH, HL and HH obtained by sequentially executing orthogonal wavelet transformation on an LL component for multiple resolution separations are synthesized through inverse wavelet transformation.
Patent reference 9 and 10, on the other hand, each disclose a method of noise removal whereby noise is removed sequentially from reduced images each generated on a temporary basis while multiple resolution transformation is in progress, i.e., low-frequency subband LL components in the case of orthogonal wavelet transformation.
In an application field in which only a specific distribution structure in a gamma ray image needs to be handled, technical issues completely different from those of standard images described above, must be addressed. Patent reference 11 discloses a method whereby the resolution transformation at a given stage is executed through orthogonal wavelet transformation and the LL, LH, HL and HH subbands are re-synthesized by executing noise removal processing on any dominant subband containing very little noise among LL, LH, HL and HH and coring any non-dominant subband so as to summarily reduce its signal value to zero.
In the method described in nonpatent reference 5 as an alternative approach to achieving a noise removal effect to the noise removal via noise removal filters described above, the original image is broken down into multiple resolution subband images in Laplacian pyramid representation and then the multiple resolution subband images are re-synthesized into an image assuming frequency characteristics different from those of the original image by weighting the subbands.
The term “orthogonal wavelet transformation” is used in this context to refer to transformation whereby a two-dimensional filter can be regarded as two one-dimensional filters separate from each other, assuming two directions perpendicular to each other to filter data along the two orthogonal directions. Accordingly, the concept of the orthogonal wavelet transformation covers bi-orthogonal wavelet transformation. This principle applies whenever the term is used in the following description.
In addition, various technologies for processing noise present in a color image directly in the real space plane via a single channel have been proposed in the related art. Such processing is normally executed by separating the data into luminance plane data and chrominance plane data. For instance, patent reference 12 discloses a technology whereby the luminance component data and chrominance component data are processed differently with adaptive smoothing executed on the luminance component by taking into consideration its directionality and isotopic smoothing executed on the chrominance component data. Nonpatent reference 6 discloses the use of a bilateral filter which is a typical example of fine adaptive noise removal filters such as edge preserving filters, instead of a simple filter such as that described above that requires separate processing assignments. In the technology disclosed in nonpatent reference 6, all the planes in the Lab space are adaptively filtered.
Hardly any in-depth research into applications in which noise is removed from a color image through multichannel frequency bands as in multiple resolution representation has been conducted to date, presumably because noise removal technologies in multiple resolution representation have been mainly developed in the field of medicine such as x-ray photography and MRI, in which monochrome images are primarily handled. An exception to that in the related art is the technology disclosed in patent reference 9 mentioned earlier, whereby noise removal via multiple resolution representation is adopted in conjunction with a digital color image. However, patent reference 9 simply presents an application example in which a single noise removal algorithm utilizing a σ filter is adopted in the processing of various planes in the LCC luminance-chrominance representation. While different σ filter threshold value settings are selected for the luminance component and the chrominance components, a single multiple resolution noise removal algorithm is utilized without alteration.    Patent reference 1: U.S. Pat. No. 4,523,230    Patent reference 2: U.S. Pat. No. 5,467,404    Patent reference 3: U.S. Pat. No. 5,805,721    Patent reference 4: U.S. Pat. No. 6,528,381    Patent reference 5: U.S. Pat. No. 5,526,446    Patent reference 6: U.S. Pat. No. 5,708,693    Patent reference 7: U.S. Pat. No. 5,461,655    Patent reference 8: U.S. Pat. No. 6,754,398    Patent reference 9: U.S. Pat. No. 6,937,772    Patent reference 10: Japanese Laid Open Patent Publication No. 2000-224421    Patent reference 11: U.S. Pat. No. 5,576,548    Patent reference 12: U.S. Pat. No. 6,618,503    Nonpatent reference 1: J. B. Weaver, X. Yansun, D. M. Healy Jr. and L. D. Cromwell, “Filtering Noise From Images With Wavelet Transforms”, Magnetic Resonance in Medicine, vol 21, no. 2, pp 288˜295, 1991    Nonpatent reference 2: R. A. DeVor and B. J. Lucier, “Fast wavelet techniques for near-optimal image processing”, IEEE Military Communications Conf. rec. San Diego, opt 11-14, 1992 vol 3, pp 1129˜1135    Nonpatent reference 3: D. L. Donoho, “De-noising by Soft-thesholding”, IEEE TransInform Theory Vol 41, pp 613˜627, 1995    Nonpatent reference 4: A. F Laine and C. Chang, “De-noising via Wavelet Transforms Using Steerable Filters” IEEE International Symposium on Circuits and Systems, Vol 3, 1995, pp 1956˜1959    Nonpatent reference 5: S. Ranganath, “Image Filtering Using Multiresolution Representations”, IEEE Transactions on Pattern and Machine Intelligence, Vol 13, No. 5, May 1991, pp 426˜440    Nonpatent reference 6: C. Tomasi et al., “Bilateral Filtering For Gray and Color Images,” Proceedings of the 1998 IEEE International Conference on Computer Vision, Bombay, India